airplane.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "1-hRgRHnP54W"
      },
      "outputs": [],
      "source": [
        "#Standard python libraries for ML project\n",
        "\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns \n",
        "import math\n",
        "import datetime\n",
        "from sklearn.preprocessing import OrdinalEncoder\n",
        "import seaborn as sns\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2 = pd.read_csv('business.csv')  # Data 1 \n",
        "df1 = pd.read_csv('economy.csv')   # Data 2\n",
        "df = pd.concat([df1,df2])          # Concatenating the above two data "
      ],
      "metadata": {
        "id": "btZ-0X61bZ2u"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lWPIH2o4MpjY",
        "outputId": "b12323a6-168f-4228-e733-0127aec36685"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "date          0\n",
              "airline       0\n",
              "ch_code       0\n",
              "num_code      0\n",
              "dep_time      0\n",
              "from          0\n",
              "time_taken    0\n",
              "stop          0\n",
              "arr_time      0\n",
              "to            0\n",
              "price         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZDQb4PpFlSdw",
        "outputId": "204d21d1-6f76-4fa6-f92e-4438b2954a19"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 300261 entries, 0 to 93486\n",
            "Data columns (total 11 columns):\n",
            " #   Column      Non-Null Count   Dtype \n",
            "---  ------      --------------   ----- \n",
            " 0   date        300261 non-null  object\n",
            " 1   airline     300261 non-null  object\n",
            " 2   ch_code     300261 non-null  object\n",
            " 3   num_code    300261 non-null  int64 \n",
            " 4   dep_time    300261 non-null  object\n",
            " 5   from        300261 non-null  object\n",
            " 6   time_taken  300261 non-null  object\n",
            " 7   stop        300261 non-null  object\n",
            " 8   arr_time    300261 non-null  object\n",
            " 9   to          300261 non-null  object\n",
            " 10  price       300261 non-null  object\n",
            "dtypes: int64(1), object(10)\n",
            "memory usage: 27.5+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "DATA PREPROCESSING"
      ],
      "metadata": {
        "id": "XoNdoJXzjccM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def rep(x):\n",
        "  a = x.replace(\"\\t\",\"\").replace(\"\\nVia\",\"\").replace(\"\\n\",\"\") \n",
        "  return a"
      ],
      "metadata": {
        "id": "JByFKvgbNT2G"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['stop1'] = df['stop'].apply(rep)\n",
        "df['stoppage'] = df['stop1'].str.split().str.get(0)"
      ],
      "metadata": {
        "id": "0TBhY5_aX5Ip"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['flight_number'] = df['ch_code'] + \"-\" + df['num_code'].astype(str)\n",
        "df['price1'] = df['price'].str.replace(\",\",\"\").astype(int)"
      ],
      "metadata": {
        "id": "wT2q6cYGbDpB"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['time_taken1'] = df['time_taken'].str.replace(\".\",\"\").str.replace(\"m\",\"\").str.replace(\"h \",\".\")\n",
        "df['time_taken1'] = df['time_taken1'].apply(lambda x :float(x))"
      ],
      "metadata": {
        "id": "0epIKScckGHK"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['class']= df['price1'].apply(lambda x :'economy' if x<18000 else 'business')"
      ],
      "metadata": {
        "id": "P8GvarN0kMxO"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "CONVERSION OF DATA TO DATE TIME \n"
      ],
      "metadata": {
        "id": "0dfTY3-0j0LM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['date'] = pd.to_datetime(df['date'])\n",
        "df['dep_time'] = pd.to_datetime(df['dep_time'])\n",
        "df['arr_time'] = pd.to_datetime(df['arr_time'])"
      ],
      "metadata": {
        "id": "5RnytTI2JAPd"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# GETTING THE DAY , DATE , MONTH , HOUR , MINUTES OF ARRIVAL AND DEPARTURE \n",
        "df['Dep_day'] = df['date'].dt.day\n",
        "df['Dep_month'] = df['date'].dt.month\n",
        "df['Dep_hour'] = df['dep_time'].dt.hour\n",
        "df['Dep_min'] = df['dep_time'].dt.minute\n",
        "df['Arr_hour'] = df['arr_time'].dt.hour\n",
        "df['Arr_min'] = df['arr_time'].dt.minute"
      ],
      "metadata": {
        "id": "zzvUpQolREPN"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "DROPPING SOME OF THE COLUMNS WHICH ARE NOT IMPORTANT"
      ],
      "metadata": {
        "id": "Iy3fmYSknbLf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop(columns = ['date','ch_code','num_code','dep_time','time_taken','stop','arr_time','price','stop1','flight_number'],inplace =True)"
      ],
      "metadata": {
        "id": "7nI-M1x2l_Xq"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "DATA VISUALIZATION "
      ],
      "metadata": {
        "id": "KRwhULPAoIEE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(df.corr(), cmap=\"YlGnBu\", annot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        },
        "id": "lsu5pxjNmeZo",
        "outputId": "75eba3b6-6a8f-41c9-b816-5e6a6e272752"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd95336f520>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"AS WE CAN SEE THAT ONE AIRLINE HAS HIGHER PRICE RANGES AS COMPARED \n",
        "TO THE OTHER AIRLINE SO INSTEAD OF ONE HOT ENCODING WE WILL USE ORDINAL ENCODER TO GET BETTER RESULTS\"\"\"\n",
        "\n",
        "sns.catplot(y = \"price1\", x = \"airline\", data = df.sort_values(\"price1\", ascending = False), kind=\"boxen\", height = 4, aspect = 3)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "hD01HJ1YGmIe",
        "outputId": "57d68cc5-73ce-4923-fd44-8db19395ac52"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.catplot(y = \"price1\", x = \"class\", data = df.sort_values(\"price1\", ascending = False), kind=\"boxen\", height = 4, aspect = 3)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "HoJbEQ67pnLt",
        "outputId": "ea20268f-ec9a-4a08-e248-a2c1eba9b512"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.catplot(y = \"price1\", x = \"stoppage\", data = df.sort_values(\"price1\", ascending = False), kind=\"boxen\", height = 4, aspect = 3)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "qBDMPYj0p7LF",
        "outputId": "1c887392-d94b-436a-d3ec-1e5ecd09352c"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "ONE HOT ENCODING AND ORDINAL ENCODING "
      ],
      "metadata": {
        "id": "OkqKYb27pt60"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.get_dummies(df, columns=['from','to']) "
      ],
      "metadata": {
        "id": "SS3bqZpcMFJr"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "le = OrdinalEncoder(categories=[['Trujet','StarAir','SpiceJet','AirAsia','GO FIRST','Indigo','Air India','Vistara']])\n",
        "df['airline'] = le.fit_transform(df[['airline']])"
      ],
      "metadata": {
        "id": "I9ScjYSLN5-U"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "le = OrdinalEncoder(categories=[['2+-stop','1-stop','non-stop']])\n",
        "df['stoppage'] = le.fit_transform(df[['stoppage']])"
      ],
      "metadata": {
        "id": "NqsNj2AzTfbK"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "le = OrdinalEncoder(categories=[['economy','business']])\n",
        "df['class'] = le.fit_transform(df[['class']])"
      ],
      "metadata": {
        "id": "V0lVgrWMUFGv"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "AFTER ALL THE DATA CLEANING AND PRE PROCESSING OUR DATA LOOKS LIKE THIS"
      ],
      "metadata": {
        "id": "TPG3cnBmqEsj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "9ONNCtPwTDR_",
        "outputId": "93d75c31-2b2f-4ee4-d48a-7793846a1eba"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   airline  stoppage  price1  time_taken1  class  Dep_day  Dep_month  \\\n",
              "0      2.0       2.0    5953          2.1    0.0        2         11   \n",
              "1      2.0       2.0    5953          2.2    0.0        2         11   \n",
              "2      3.0       2.0    5956          2.1    0.0        2         11   \n",
              "\n",
              "   Dep_hour  Dep_min  Arr_hour  ...  from_Delhi  from_Hyderabad  from_Kolkata  \\\n",
              "0        18       55        21  ...           1               0             0   \n",
              "1         6       20         8  ...           1               0             0   \n",
              "2         4       25         6  ...           1               0             0   \n",
              "\n",
              "   from_Mumbai  to_Bangalore  to_Chennai  to_Delhi  to_Hyderabad  to_Kolkata  \\\n",
              "0            0             0           0         0             0           0   \n",
              "1            0             0           0         0             0           0   \n",
              "2            0             0           0         0             0           0   \n",
              "\n",
              "   to_Mumbai  \n",
              "0          1  \n",
              "1          1  \n",
              "2          1  \n",
              "\n",
              "[3 rows x 23 columns]"
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              "      <th>from_Kolkata</th>\n",
              "      <th>from_Mumbai</th>\n",
              "      <th>to_Bangalore</th>\n",
              "      <th>to_Chennai</th>\n",
              "      <th>to_Delhi</th>\n",
              "      <th>to_Hyderabad</th>\n",
              "      <th>to_Kolkata</th>\n",
              "      <th>to_Mumbai</th>\n",
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              "<p>3 rows × 23 columns</p>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W2MEDf5MWYOQ",
        "outputId": "beffd97f-6b4b-490c-fb26-5220c05475ac"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['airline', 'stoppage', 'price1', 'time_taken1', 'class', 'Dep_day',\n",
              "       'Dep_month', 'Dep_hour', 'Dep_min', 'Arr_hour', 'Arr_min',\n",
              "       'from_Bangalore', 'from_Chennai', 'from_Delhi', 'from_Hyderabad',\n",
              "       'from_Kolkata', 'from_Mumbai', 'to_Bangalore', 'to_Chennai', 'to_Delhi',\n",
              "       'to_Hyderabad', 'to_Kolkata', 'to_Mumbai'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "TRAIN TEST SPLIT AND TRAINNING OF THE MODELS"
      ],
      "metadata": {
        "id": "HsOnT1x4qzYq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X = df.drop(columns =[\"price1\"])\n",
        "y = df['price1']\n",
        "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size =0.2,random_state=42)"
      ],
      "metadata": {
        "id": "OO-c6G1aJObk"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "from sklearn.ensemble import ExtraTreesRegressor\n",
        "from sklearn.metrics import r2_score"
      ],
      "metadata": {
        "id": "w6w6JkqVHHM0"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "modelKNN = KNeighborsRegressor()\n",
        "modelETR = ExtraTreesRegressor()"
      ],
      "metadata": {
        "id": "oo4SemviHKHg"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "MM = [modelETR,modelKNN]\n",
        "\n",
        "MM1 = [str(x) for x in MM]\n",
        "a = []\n",
        "for model in MM:\n",
        "  \n",
        "    model.fit(X_train, y_train)\n",
        "    \n",
        "    y_pred = model.predict(X_test)\n",
        " \n",
        "    a.append(r2_score(y_test , y_pred))"
      ],
      "metadata": {
        "id": "g1Ajs5woH5r4"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "CONVERTING THE R2_SCORE INTO A DATAFRAME "
      ],
      "metadata": {
        "id": "jAZLksxXqtRr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "d = pd.DataFrame({\"Model_name\":MM1,\"r2_score\":a})\n",
        "d\n"
      ],
      "metadata": {
        "id": "iVHYo0xeKqko",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "outputId": "e4d87b29-9d46-475a-a225-a2a500a37625"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Model_name  r2_score\n",
              "0  ExtraTreesRegressor()  0.954943\n",
              "1  KNeighborsRegressor()  0.441214"
            ],
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              "          const element = document.querySelector('#df-34a88267-61bd-4d3d-a86d-f662c3d106f2');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "CHOOSING EXTRA TREE REGRESSOR AND DUMPING IT USING PICKLE LIBRARY"
      ],
      "metadata": {
        "id": "rT1lKkzB8RvN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "pickle.dump(modelETR,open('model1,pkl','wb'))"
      ],
      "metadata": {
        "id": "0wIWCn_I79Ap"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}